Autonomous supply and distribution chain

ABSTRACT

Methods and systems for an autonomous supply and distribution chain management network are disclosed. A server may control and coordinate the processes involved in distributing a product from suppliers to the customers, including generation of purchase orders and payment of invoices. A defined set of interactions may occur in a particular sequence and at designated times that may permit the chain to be synchronized between a customer and a supplier. Unlike a regular supply and distribution chain, in which human beings decide vehicle or asset compatibility types, the autonomous chain of the present invention may maintain a compatibility database within the platform, as well as detailed information about each asset and how it can function interactively with the others. The invention may also allow for dynamic modification of transit operations to alter one or more destinations of the inventory while it is in transit to a new location at any time.

CLAIMS OF PRIORITY

This patent application is a continuation and claims priority from:

-   (1) U.S. utility patent application Ser. No. 15/849,643, entitled    ‘Autonomous supply and distribution chain’ filed on Dec. 20, 2017.-   (2) U.S. utility patent application Ser. No. 15/456,521, entitled    ‘Complex dynamic route sequencing for multi-vehicle fleets using    traffic and real-world constraints’ filed on Mar. 11, 2017, which    claims benefit of U.S. provisional patent application No.    62/307,402, entitled ‘Complex dynamic route sequencing for    multi-vehicle fleets using traffic and real-world constraints’,    filed Mar. 11, 2016.-   (3) U.S. utility patent application Ser. No. 15/599,426, entitled    ‘Methods and systems for managing large asset fleets through a    virtual reality interface’, filed on May 18, 2017, which claims    benefit of U.S. provisional patent application No. 62/338,487,    entitled ‘Method and system for managing large vehicle fleets    through a virtual reality environment’, filed May 18, 2016.-   (4) U.S. utility patent application Ser. No. 15/673,394, entitled    ‘Methods and systems for detecting and verifying route deviations’,    filed on Aug. 9, 2017, which claims benefit of U.S. provisional    patent application No. 62/372,313, entitled ‘Methods and systems for    detecting and verifying route deviations’, filed Aug. 9, 2016.-   (5) U.S. provisional patent application No. 62/436,449, entitled    ‘Autonomous supply and distribution chain’, filed Dec. 20, 2016.

FIELD OF TECHNOLOGY

This disclosure relates generally to techniques for intelligentautonomous supply and distribution chain network, which centralizes manyoperations including management of the movement of inventory through thechain.

BACKGROUND

A supply and distribution chain can refer to a system or group ofentities, activities, information, and/or assets involved in thedelivery of products or services from a supplier to a customer.Activities can involve some transformation of raw materials or data intoa finished product or service that can ultimately be delivered orotherwise provided to an end user. The chain can exist within a singleentity or across various business entities within, e.g., a particularproduct or service industry.

Modern channels of trade, including international supply anddistribution chains, are often highly complex, span thousands of milesand multiple transportation modes and convey valuable goods from sourcesto destinations all over the world. A typical supply and distributionchain often begins at a manufacturing plant, where goods are fabricatedand loaded into shipping containers for transportation by truck or trainto a sea port. At the port, the shipping containers are loaded ontoships and transported across various bodies of water. Once they reachtheir destination, the ships are unloaded, and the shipping containersagain are shipped over land by truck or train to one or more rail yards,and then to distribution centers. The goods are then typically broken upinto smaller lots, perhaps into separate pallets or boxes and loadedonto trucks for their final destinations, which are often retail stores,customer locations, or other manufacturing plants.

Currently, expensive and labor-intensive tracking systems exist foridentifying and locating goods in such chains. However, these systemsfail to offer end-to-end transit operation optimization and requireexcessive human intervention and maintenance, severely diminishing theireffectiveness. Various conventional asset tracking systems are able tocover only portions of a chain, and typically handle many functions ascompletely independent events without communication (and with humanoperators). For example, fulfillment may be handled independently ofsupplier payments, or even order management. In addition, many dates aremanually entered, tracked and changed according to the expected deliverystatus of the product ordered. This is a very costly and time consumingtask as the sequence of information, products, and currency can changedepending upon the needs of the specific customers, suppliers andlogistics providers that are using the network. Suppliers and customersoften find themselves paying higher prices, being short of products intimes of high demand, forecasting needs inaccurately, and creating slowmoving inventories because these legacy systems and methods do not havethe resources or time to manage their supply and distribution chainproperly. There is a need for an autonomous supply and distributionchain management system that can provide detailed information about eachasset and how it can function interactively with the other assets in thesupply chain, and optimizes transit operations based on the assetcompatibilities and various constraints.

SUMMARY

In one aspect, the present invention discloses a system and a method forautonomous detection of asset compatibility based on a target transitoperation, and to optimize a route based on the compatibility data.Unlike a regular supply and distribution chain, in which human beingsdecide vehicle or asset compatibility types, e.g., drivers, roads,buildings, marine ships, and load docks, the autonomous supply anddistribution chain of the present invention may maintain a centralizedasset compatibility database within the platform, as well as detailedinformation about each asset and how it can function interactively withthe other assets in the supply and distribution chain. For example, theautonomous supply and distribution chain may mix asset classes, such asa human driven vehicle that delivers or picks up from a fully autonomouswarehouse, or vice versa, such as a fully autonomous vehicle thatdelivers or picks up from a human operated warehouse. In the event thatvarious assets are misclassified or are determined to be incompatibleduring the execution phase in the supply and distribution chain, thesystem and the method may compensate using various methods and accesssensors present on each machine in the chain, such as, e.g., machinevision, fuzzy logic, risk analysis and scoring, as a fallback to try tocomplete the necessary step. An optimization algorithm may compute anddetermine the combination or mixture such that the logistics operationis maximized, and may involve dynamically updating and changing thecombination or mixture as new data are received and analyzed while thetransit operation is in progress. Asset compatibility specifications maybe defined by the manufacturer of the asset, and may be overridden bythe administrator. The autonomous platform may use artificialintelligence to reconfigure itself and re-coordinate all the assets inits control to fully maximize the efficiency of the supply anddistribution chain, including running its own multivariate experimentsunknown to human operators, making modifications to these experimentsover time based on quantitative, qualitative, and other types of logic.The system and the method may use artificial intelligence to predictwhich autonomous supply and distribution chain assets are likely tounderperform based on available data, such as, e.g., telematics data,sensor data, onboard diagnostics data, recent exposure to hazardous roadconditions, and a series of inclement weather events.

In another aspect, the present invention discloses a system and a methodfor providing inventory location identification, and dynamicmodification of transit operations or routes to alter one or moredestinations of the inventory while it is in transit to a new locationat any time. Dynamic modification may increase performance where itmatters in the supply and distribution chain by adding flexibility intothe system such that delays occurring from unexpected events areminimized. For example, an unforeseen weather event may prevent aparticular asset from completing its delivery job; however, the systemmay autonomously and dynamically modify the route to allow for anotherasset not impacted by the weather event to deliver a same or similaritem do the end user. Due to the rapid and fluid environment of modernwarehouses and transportation services, workers often receiveinstructions to change the destination of inventory that has alreadybeen received and is in the process of being transferred to anotherlocation. However, once the inventory has been picked-up from itsoriginal location, it may not be possible previously to determine thevehicle used for transport and its current location until the inventoryarrives at a warehouse and/or until an inventory database is updated. Ina busy delivery or supply and distribution chain environment, this canresult in substantial delays and inefficiencies due to the inability torapidly locate inventory and respond to urgent stock needs or change oftransfer orders.

In yet another aspect, the present invention discloses a system and amethod for an autonomous supply and distribution chain managementnetwork. A supply and distribution chain may be any and all activitiesassociated with defining, designing, producing, receiving, delivering,monitoring, storing and using the components and sub-components used inmanufacturing a product. A server may control and coordinate theprocesses involved in distributing the product from suppliers to thecustomers, including generation of purchase orders and payment ofinvoices. Once a product is qualified, a defined set of interactions mayoccur in a particular sequence and at designated times that may permitthe supply and distribution chain to be synchronized between a customerand a supplier. Such a well synchronized chain may comprise minimalinventories and short reaction times to efficiently handle transferorder changes. The network may comprise modules pertaining to variousaspects and operations of a supply and distribution chaincommunicatively coupled to a centralized server, and may include, e.g.,sourcing, procurement, conversion, logistic, and collaboration. Theserver may manage individual modules independently or simultaneouslysuch that there is coordination in the supply and distribution chainnetwork. For example, the supply and distribution chain network mayconnect actors having various roles, such as, e.g., dock workers,longshoreman, field agents, customs agents, freight forwarders,customers, suppliers, logistics providers, carriers and financialinstitutions. The system and the method of the present invention maycreate a network which supports customers requesting a same or similarproduct, and may realize lower costs and increased flexibility even inchanging supply demands.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments are illustrated by way of example and are notlimited to the figures of the accompanying drawings, in which, likereferences indicate similar elements.

FIG. 1 is a schematic diagram of an example supply and distributionchain.

FIGS. 2A-B illustrate embodiments of various shipping methods.

FIG. 3 is a flowchart of a method for detecting anomalies in themovement of an asset, according to at least one embodiment.

FIG. 4 illustrates a fleet management system, according to at least oneembodiment.

FIG. 5 is a schematic diagram of a location transmitting device,according to at least one embodiment.

FIG. 6 is a schematic illustration of a central server, according to atleast one embodiment.

FIG. 7 illustrates a computing environment of a mobile device forimplementing various aspects of the invention.

FIG. 8 is a flowchart of a method for a supply and distribution chain,according to at least one embodiment.

FIG. 9 illustrates an example relational map of a company's operationunits, according to at least one embodiment.

FIG. 10 illustrates a table of operational constraints corresponding toan operation unit, according to at least one embodiment.

FIG. 11 illustrates an example process flowchart performed for providingan optimized transit operation, according to at least one embodiment.

FIG. 12 is a schematic diagram of a distribution center comprising awarehouse, according to at least one embodiment.

FIG. 13 is a flowchart of a method for monitoring inventory location,according to at least one embodiment.

FIG. 14 is a schematic diagram of a tracking device coupled to acontainer of an inventory, according to at least one embodiment.

FIG. 15 is a flowchart of a method for tracking an inventory using atracking device, according to at least one embodiment.

FIG. 16 illustrates a supply chain management network, according to atleast one embodiment.

FIG. 17 is a flowchart of a method for processing customer demand fordirect material, according to at least one embodiment.

DETAILED DESCRIPTION

Disclosed are systems and methods for autonomous optimization of supplyand distribution chain transit operations, including management of themovement of inventory through the chain. Although the presentembodiments have been described with reference to specific exampleembodiments, it will be evident that various modifications and changesmay be made to these embodiments without departing from the broaderspirit and scope of the various embodiments. In addition, the componentsshown in the figures, their connections, couples, and relationships, andtheir functions, are meant to be exemplary only, and are not meant tolimit the embodiments described herein.

FIG. 1 is a schematic diagram of an example supply and distributionchain. Products and goods may be produced at a manufacturing plant andtransported to a first port 110. The first port 110 may comprise ashipping container 102 that is loaded by a first crane 104 onto a vessel106. The first crane 104 may be an autonomous crane or robot, and/or ahuman operated crane or robot. The vessel 106 may be any type of vehicleused to convey the container 102 oversea, such as, e.g., a marine shipor an airplane. The vessel 106 mat then transit an ocean or sea 108, andarrives at a second port 110.

A second crane 112 may unload the shipping container 102 from the vessel106, and then load the container 102 onto a train 114 comprising severalcars. The train 114 may transit tracks 116 and arrive at a yard 118,where the shipping container 102 is offloaded and stored in a line withother shipping containers. The shipping container 102 may be outfittedwith wheels to allow it to be towed. Trailers that are not shippingcontainers may also be present at the yard 118. A first site vehicle120, such as, e.g., a crane or a mule, may move and arrange the variousshipping containers as desired. Finally, the shipping container 102 maybe hitched to a truck 122. The truck 122 may transit a roadway 124 andarrive at a distribution center 126. In some embodiments, the shippingcontainer 102 may skip the distribution center 126 and is delivereddirectly to a consumer or another facility. The distribution center 126may comprise a warehouse 128, and serve as a terminus comprising variousshipping containers. Different warehouses may comprise specializedcapabilities, such as, e.g., temperature control or additional security.A second site vehicle 130, such as, e.g., a forklift, may be employed tounload the shipping containers and move small units of goods containedtherein into crates or boxes or on pallet 132. In some embodiments thegoods may comprise livestock. The goods may then be delivered to retailstores or customer locations via, e.g., a delivery truck or an aerialvehicle, such as a drone 134. Warehouse 128 may also comprisecross-docking, such as for transporting of the goods to other warehousesor distribution centers.

FIGS. 2A-B illustrate embodiments of various shipping methods. In FIG.2A, two shipping methods between a source location 200 and a destinationlocation 202 may correspond to the same mode or class of transitprovided by two different carriers or the same carrier. For example,both methods may correspond to a surface-based mode of transit, such as,e.g., ground service. By contrast, FIG. 2B shows both shipping methodscorresponding to different modes of transit, also either by twodifferent carriers or the same carrier.

Multiple possible combinations of carriers and classes or modes oftransit may exist between source 200 and destination 202. For example,common carriers may include DHL, the United States Postal Service(USPS), FedEx, and other shipping companies as well as the postalservices of other countries. Additionally, privately contractedcarriers, such as, e.g., company-owned fleets, or private drivers of anauction-based or on-demand economy platform or exchange, may be employedto implement the transit as an alternative or complement to the use ofcommon carriers. In some embodiments, for example, where source 200 anddestination 202 are located within a single facility, the shippingmethods may include various small-scale procedures and resources formoving materials, such as, e.g., forklifts, mules, manned push carts,trucks, and conveyor systems. Generally, a shipping or transportationmethod may encompass any suitable method for conveying tangible goodsfrom one location to another on a large or small geographic scale,including common and/or private carriers, and land, air, and/or seamodes.

In some embodiments, each shipping method may be independently modeledto yield predictions regarding transit times from source 200 todestination 202 to ideally represent the actual behavior of the shippingor transportation mode. A predictive model of a shipping method may beconstructed through analysis of empirical transit data of actualshipments from source 200 to destination 202 using collected trackingdata documenting the progress of items in transit. In addition, thepredictive model may be built based on simulated or anticipated demand,such as, e.g., a new business unit with no historical actual shipmentdata available. Common carriers typically make such tracking dataavailable to shippers and customers via web-service interfaces. Trackingdata may, for example, indicate the date and time at which an item wasaccepted by the carrier for shipment from source 200 and the date andtime of delivery at destination 202. During intra-facility materialshandling, empirical transit data may be collected via, e.g., bar code orRFID scanning devices, and may operate in conjunction with a locatorsystem, such as, e.g., a GPS, an inertial navigation system (INS), abeacon, or an LED location scanning system. Over time, empiricaltracking data may yield a substantial number of data points regardingshipments from source 200 to destination 202 using a particular shippingmethod. Tracking data and other shipping or conveyance data may berepresentative of a number of different transit characteristics ofconveyance of goods from source 200 to destination 202. A transitcharacteristic may comprise any measurable or empirically observableaspect of a route between source 200 and destination 202. For example,transit characteristics may comprise transit latency and/or probabilitydistribution of a measured metric, such as, e.g., shipping rates and/ora distribution of such rates corresponding to an item's dimensions andweight. A transit characteristic may be grouped together according to asimilarity criterion, such as, e.g., a transit latency value, such thatthe grouping occurs when the similarity criterion matches exactly orwithin a given threshold of difference, e.g., an absolute number ofunits, a percentage, or a range. Through analyzing the data, the systemand the method may be able to detect anomalies in the movement andhandling of an asset in the transit operation by comparing a collecteddata point to the predictive model.

FIG. 3 is a flowchart of a method for detecting anomalies in themovement of an asset, according to at least one embodiment. Operation310 may attach a tag to an asset to be tracked in a transit operation.The tag may provide identification and location data of the asset inaddition to various sensory data for monitoring the asset or theinventory being transported by the asset. Operation 320 collects datafrom the tag attached to the asset as it moves from one location toanother and recording the time of the reading. The data may be sent to acentral server for processing. Operation 330 builds a predictive modelfrom the collected data. For example, the data may be classified intoclasses with common properties. Operation 340 compares a subsequentlycollected data point to the predictive model. Operation 350 determineswhether the subsequently collected data point falls within a parameterof the predictive model, such as, e.g., within a geographic radius. Forexample, if the subsequently collected data point falls within theparameter, the data point may be considered normal; on the other hand,if the data point falls outside of the parameter, the data point may beconsidered abnormal and may require further analyses or processing.Operation 360 may add the subsequently collected data point to thepredictive model and thereby generating a new predictive model.

FIG. 4 illustrates a fleet management system, according to at least oneembodiment. As a plurality of vehicles or assets proceeds through itsassigned routes, they may be equipped with one or more sensors, such as,e.g., a telematics device or a location transmitting devices. Thelocation transmitting devices may be able to send location informationto a central server as each vehicle progresses and the estimated timesof arrival at a destination may be continually updated. Vessel A 400,vessel B 402, and vessel C 404 may be communicatively coupled to amobile device 406 and a central server 408 via a communications network410, such as, e.g., the Internet, an Intranet, a hybrid-cloud network,or other suitable network. In addition, vessel A 400, vessel B 402,vessel C 404, mobile device 406, and central server 408 may beconfigured for storing data to an accessible database of, oralternatively, stored remotely from, the central server 408. The sensorsmay generate telematics data associated with, e.g., engine ignition,engine speed, vehicle speed, vehicle location, status of vehicle seatbelts engagement, doors, handles, distance traveled, throttle position,brake pedal position, parking brake position, onboard sensory data,e.g., temperature, cargo hold utilization, onboard weight,self-diagnostic, and/or data associated with the environment in whichthe vehicle is operating, such as, e.g., a sensor for detectingtemperature, moisture, pressure, or weather events. According to variousembodiments, an on/off sensor, which may register a voltage amount thatcorresponds with an on or off condition, may be disposed within an assetfor collecting data. For example, a seat belt sensor may register 0Vwhen the seat belt is disengaged and 12V when the seat belt is engaged.This may be sufficient for the seat belt sensor in particular becausethe seat belt is either engaged or disengaged at all times. In otherembodiments, a variable voltage sensor, which may register variations involtage, may also be disposed within the asset for collecting data. Forexample, the engine speed sensor may detect the speed of the engine inrevolutions per minute (RPM) by registering a particular voltage thatcorresponds to a particular RPM reading. The voltage of the sensor mayincrease or decrease proportionately with increases or decreases in theengine RPM.

The analysis of data collected by the sensors may be performed bysoftware or an algorithm executed by a processor coupled with a memory,such as from the central server 408. The central server 408 may beconfigured to analyze and identify received data indicating variousinefficiencies, safety hazards, or security hazards present in thetransit operation protocol. In some embodiments, the sensors maytransmit telematics data via network 410 to the mobile device 406operated by a driver associated with vessel A 400, vessel B 402, and/orvessel C 404.

FIG. 5 is a schematic diagram of a telematics device attached to, orembedded with, a supply chain asset, according to at least oneembodiment. The telematics device may comprise a receiver 500, aprocessor 502, a communication module 504, and a memory 506. Thereceiver 500 is operable to determine location information of a vehicleor mobile device using satellite network data. Typically, this datacomprises coordinates, speed, and direction of the telematics device.The processor 502 may receive location information from the receiver 500and transmit to a central server using the data communication module 504at predetermined time intervals. The communication module 504 ispreferably a cellular transmitter operable to send data from thetelematics device over a cellular network, or alternatively, over asatellite network or wireless network connection. The data communicationmodule 504 may comprise a receiver to receive information and data fromthe central server. The telematics device may be configured to control avariety of vehicle sensors, such as, e.g., capture and store vehicletelematics data generated by the sensors for analyzing. In somesituations, the telematics data may be unable to be immediatelytransmitted, such as, e.g., due to unavailability of Internetconnectivity. In such cases, the system may temporarily store, such as,e.g., accumulate or queue, data to its local computer storage andretransmit data as soon as stable Internet connectivity isre-established. The accumulated data may then get removed from memory506 after successful transmission to the central system server.

FIG. 6 is a schematic illustration of a central server, according to atleast one embodiment. The central server may be a conventional computeroperable to execute coded instructions comprising a processor 600, amemory storage device 602, a program 604, an input device 606, and adisplay device 608. The processor 600 may be any processing unit that istypically known in the art with the capacity to run computer program604, and may be communicatively coupled to memory storage 602, such as,e.g., a local hard-disk. Input device 606 may be any device suitable forinputting data into the central server, such as, e.g., a keyboard and amouse, and may be communicatively coupled to processor 600. Displaydevice 608 may be any suitable device communicatively coupled toprocessor 600 operable for displaying data to a user or administrator ofthe system. Program 604 may be stored in memory storage 502, and may beoperable to provide instructions to processor 600. Database 610 may becommunicatively coupled to the central server and operable to storedata. Typically, database 610 comprises a number of records 612corresponding with transit operations, such as, e.g., a delivery job, aninitial pick-up location, a driver identifier, a vehicle position, and atime stamp.

In some embodiments, in addition to receiving telematics data fromvehicle sensors, a mobile device may be configured to collect andtransmit telematics data on its own. For example, the mobile device mayinclude a location determining device, such as a GPS, for providinglocation information of the driver, as opposed to location informationassociated with the vehicle or vessel.

FIG. 7 illustrates a computing environment of a mobile device forimplementing various aspects of the invention. The processing unit 731may be any of various available processors, such as singlemicroprocessor, dual microprocessors or other multiprocessorarchitectures. The system bus 730 may be any type of bus structures orarchitectures, such as 12-bit bus, Industrial Standard Architecture(ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA),Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), PeripheralComponent Interconnect (PCI), Universal Serial Bus (USB), AdvancedGraphics Port (AGP), Personal Computer Memory Card InternationalAssociation bus (PCMCIA), or Small Computer Systems Interface (SCST).

The system memory 732 may include volatile memory 733 and nonvolatilememory 734. Nonvolatile memory 734 may include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory733, may include random access memory (RAM), synchronous RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), or directRambus RAM (DRRAM).

The mobile device also includes storage media 736, such asremovable/non-removable, volatile/nonvolatile disk storage, magneticdisk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-100drive, flash memory card, memory stick, optical disk drive such as acompact disk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). A removable or non-removable interface 735 may be used tofacilitate connection.

The mobile device may further include software to operate in thecomputing environment, such as an operating system 711, systemapplications 712, program modules 713 and program data 714, which arestored either in system memory 732 or on disk storage 736. Variousoperating systems or combinations of operating systems may be used.

Input device 722 may be used to enter commands or data, and may includea pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, sound card, digital camera, digital video camera, webcamera, and the like, connected through interface ports 738. Interfaceports 738 may include a serial port, a parallel port, a game port, auniversal serial bus (USB), and a 1394 bus. The interface ports 738 mayalso accommodate output devices 721. For example, a USB port may be usedto provide input to the mobile device and to output information from themobile device to an output device 721. Output adapter 739, such as videoor sound cards, is provided to connect to some output devices such asmonitors, speakers, and printers.

The position detection device 724 may be a device that communicates witha plurality of positioning satellites, e.g., GPS satellites, todetermine the geographical location of the mobile device, and thus theuser. To determine the location of the user, the position detectiondevice 724 searches for and collects GPS information or signals fromfour or more GPS satellites that are in view of the position detectiondevice 724. Using the determined time interval between the broadcasttime and reception time of each signal, the position detection device724 may calculate the distance of the user relative to each of the fouror more GPS satellites. These distance measurements, along with theposition and time information received in the signals, allow theposition detection device 724 to calculate the geographical location ofthe user.

The mobile device may be communicatively coupled to remote computers,such as, e.g., the platform, through the network. The remote computersmay comprise a memory storage device, and may be a personal computer, aserver, a router, a network PC, a workstation, a microprocessor basedappliance, a peer device or other common network node and the like, andtypically includes many or all of the elements described relative tocomputer 601. Remote computers may be connected to the mobile devicethrough a network interface and communication connection 737, with wireor wireless connections. A network interface may be communicationnetworks such as local-area networks (LAN), wide area networks (WAN) orwireless connection networks, or by cellular network communicationtechnology. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet/IEEE 1202.3,Token Ring/IEEE 1202.5 and the like. WAN technologies include, but arenot limited to, point-to-point links, circuit switching networks likeIntegrated Services Digital Networks (ISDN) and variations thereon,packet switching networks, and Digital Subscriber Lines (DSL).

In some embodiments, the present invention discloses a system and amethod for a connected and autonomous supply and distribution chain,which can analyze and incorporate business intelligence data into anoptimization algorithm and may compute feasibility, balance resourcesbased on user-defined settings or machine-learning processes, and/orminimize operating costs of a logistic operation. For example, adetermination of the additional amounts of risk and cost may be assessedfor assigning additional assets to a job, such as, e.g., additionalforklifts to move cargo within a warehouse, and may be weighed with thebenefit of time reduction in completing the task. In general, businessintelligence may refer to theories, methodologies, architectures, and/ortechnologies that transform raw data into meaningful and usefulinformation for business purposes and may include, for example,multidimensional aggregation and allocation; denormalization, taggingand standardization; real-time reporting with analytical alerts;interfacing with unstructured data sources; statistical inference andprobabilistic simulation operations; key performance indicatoroptimization; version control and process management; and open itemmanagement. Thus, business intelligence data may be utilized to providehistorical, current, and/or futuristic or predictive views of theoperation of a fleet, and may include, e.g., reporting, onlineanalytical processing, analytics, data mining, process mining, complexevent processing, business performance management, benchmarking, textmining, predictive analytics, and prescriptive analytics. Businessintelligence tools reflective of the aforementioned businessintelligence technologies may refer to a type of application softwaredesigned to retrieve, analyze and report data for achieving suchbusiness intelligence. Examples of business intelligence tools mayinclude, but are not limited to, populating spreadsheets, reporting andquerying software, online analytical processing, digital dashboards,business performance management, and decision engineering.

Typically, a delivery fleet will begin with a number of delivery jobs toperform with a set number of assets in the fleet. Each delivery job willusually comprise a pickup location, where a load or cargo has to beloaded onto the vehicle, and a delivery location, where a load or cargohas to be delivered to, and unloaded from, the asset. For example, whena driver starts to travel to a destination, a vehicle trip begins. Whenthe driver reaches the destination and delivers the package, the vehicletrip ends. Thus, a full delivery route will often comprise a number ofvehicle trips or delivery jobs. Telematics data may be captured by afleet management system from the vehicles in the fleet as they executevarious delivery routes.

The platform may provide for the scheduling of transits, e.g.,conveyance and customer delivery, using estimated arrival times that arecontinually updated with real world data while the transit progresses.Location information of assets may allow comparison of the actualprogress of the delivery asset or vehicle to its predicted progress andupdate the estimated times of arrival. As such, inaccuracies in initialestimates and unforeseen circumstances are taken into account by thesystem and the method, and the scheduling of subsequent delivery jobsmay use these timely estimates to increase the accuracy of the overallscheduling. For example, the loading of the delivery asset or vehicle ata loading destination may have taken more or less time than originallyestimated, or the asset for reasons such as traffic or delay could takelonger to travel to and from the destination. Alternatively, the driverof the asset could have stopped, went down a different route, drovefaster than the estimated speed, or simply got lost. Periodicallyretrieving location information of the asset may nullify and account theunexpected events to provide for more accurate calculations and updatesof estimated times of arrival at subsequent destinations.

FIG. 8 is a flowchart of a method for a supply and distribution chain,according to at least one embodiment. The method may be repeatedindefinitely until all delivery jobs of an operation are complete.Operation 810 receives telematics data such as coordinates of anoperation's asset, e.g., trucks, trains, ships, airplanes, micro-drones,delivery drones, humanoid robots, self-driving or self-guided car robotdrones. Location information may comprise an asset identifier,coordinates, direction, and speed. Operation 820 calculates deliveryduration for each destination or delivery job. A job that is in progressmay comprise an estimated remaining time to completion, and a job thathas not begun may comprise an estimated total time required to complete.Delivery duration may typically be the time to unload or deliver aninventory at a destination, and/or load the asset for transporting to anext destination. Operation 830 calculates estimated arrival times toall pending destinations. For example, a delivery job that is currentlyin progress will comprise an estimated arrival time of zero. Operation840 determines delivery routes for all assets to fulfill all remainingdestinations. Assets may be assigned destinations or delivery jobs basedon estimated delivery duration from operation 820 such that multipleassets arrive at the destination or delivery job when the previousasset's delivery duration reaches zero. Typically, there will be moredestinations or delivery jobs than assets such that when an assetfinishes with a destination or job, it may be dispatched to anotherdestination or job. When an asset has completed a delivery job, it maybe assigned another delivery job based on the information known aboutall of the other remaining unassigned or assigned delivery jobs beingcompleted by the other assets, such as, e.g., delivery duration,remaining route, completed route, asset compatibility, operationalconstraints, environment constraints, geographic constraints andinventory compatibility. Operation 850 repeats operations 810 to 850.

In at least one embodiment, the present invention discloses a system anda method for autonomous detection of asset compatibility based on atarget transit operation, and to optimize a route based on thecompatibility data. Unlike a regular supply and distribution chain, inwhich human beings decide vehicle or asset compatibility types, e.g.,drivers, roads, buildings, marine ships, and load docks, the autonomoussupply and distribution chain of the present invention may maintain acentralized asset compatibility database within the platform, as well asdetailed information about each asset and how it can functioninteractively with the other assets in the supply and distributionchain. For example, the autonomous supply and distribution chain may mixasset classes, such as a human driven vehicle that delivers or picks upfrom a fully autonomous warehouse, or vice versa, such as a fullyautonomous vehicle that delivers or picks up from a human operatedwarehouse. In the event that various assets are misclassified or aredetermined to be incompatible during the execution phase in the supplyand distribution chain, the system and the method may compensate usingvarious methods and access sensors present on each machine in the chain,such as, e.g., machine vision, fuzzy logic, risk analysis and scoring,as a fallback to try to complete the necessary step. An optimizationalgorithm may compute and determine the combination or mixture such thatthe logistics operation is maximized, and may involve dynamicallyupdating and changing the combination or mixture as new data arereceived and analyzed while the transit operation is in progress. Assetcompatibility specifications may be defined by the manufacturer of theasset, and may be overridden by the administrator. The autonomousplatform may use artificial intelligence to reconfigure itself andre-coordinate all the assets in its control to fully maximize theefficiency of the supply and distribution chain, including running itsown multivariate experiments unknown to human operators, makingmodifications to these experiments over time based on quantitative,qualitative, and other types of logic. The system and the method may useartificial intelligence to predict which autonomous supply anddistribution chain assets are likely to underperform based on availabledata, such as, e.g., telematics data, sensor data, onboard diagnosticsdata, recent exposure to hazardous road conditions, and a series ofinclement weather events.

The asset compatibility calculations and determinations may be based onoperation unit data including, but not limited to, e.g., driver data,road data, building data, vehicle data, and environmental data. Driverdata may identify various driver profiles that may correspond to knownor recorded driving behavior of the driver, e.g., aggressive or passive.Road data may identify the different road networks in a driving route,e.g., highway, freeway, slow zone, parking structure, overpass andunderpass. The road data may also comprise data corresponding to qualityof road, such as if a specified road is rough or smooth. Building datamay identify the structure involved, e.g., high-rise building,warehouse, or residential structure. Vehicle data may identify a meansof travel, e.g., aerial, terrestrial, or marine, and whether the vehicleis autonomous, e.g., self-driving or human operated. Vehicle data mayalso include maintenance data, such as, e.g., maintenance requirementsand schedules, for each vehicle. Environmental data may identify theasset or vehicle's carbon footprint, e.g., carbon monoxide emissions.

The system and the method may map and analyze existing relationshipsbetween the operation units of a fleet and the one or more assets orelements associated with each of the units, and may provide transitroute recommendations based on compatibility of the assets or elements.A supply and distribution chain management server may comprise one ormore relational databases that organize operation units utilized in atransit operation protocol. Moreover, a computing device hosting anecosystem application may be configured to access the relationaldatabase, and based on the associations, determines compatible assets orelements to carry out the functions of each step of the transitoperation, e.g., determines the type of vehicle suitable to engage anddisplace a cargo of a particular size and weight to a particulardistance and elevation. As a result, autonomous and intelligentrecommendations can be provided to a transit operation regarding the useand/or implementation of technology assets or elements in order tofacilitate a uniform operational ecosystem. Such uniformity may beachieved by incorporating the least amount of different asset or elementfunctions possible in order to improve efficiency and reduce theinstances of, e.g., redundant data and protocol translations orconversions due to incompatibilities. Uniformity can also be achieved byimplementing assets with a preferred data, software, and/or hardwarecompatibility, e.g., requiring the least amount of extraneous resourcesto achieve compatibility.

Besides promoting a more efficient and uniform transit operation,existing technology assets or elements can be analyzed and validated asto their usefulness to a company, allowing outdated or poor-performingtechnology assets to be replaced or phased out in light of moreinnovative, efficient, or otherwise more appropriate assets. Thus, earlydetection of issues in the supply and distribution chain can prevent orhead off unwanted vendor or technology entrenchment. Any changes to thechain can be planned and implemented more quickly and efficiently,allowing a company to adapt to changing industry conditions, and avoidunwanted discontinuities in its operations.

FIG. 9 illustrates an example map of a company's operation units,according to at least one embodiment. This map can be utilized as aback-end support upon which queries and/or recommendations can be based.Administrators of a supply and demand chain may be presented with avisual representation of such a map to provide a view of a company'sassets or elements and their interrelationships. The map may compriseoperation unit A 900, operation unit B 902, operation unit C 904, andoperation unit D 906 corresponding to, e.g., driver data, road data,building data, vehicle data, and environmental data. Each operation unitmay be associated with one or more assets or elements. For example,driver data may identify various driver profiles that may correspond toknown or recorded driving behavior of the driver, e.g., aggressive orpassive. Road data may identify the different road networks in a drivingroute, e.g., highway, freeway, slow zone, parking structure, overpassand underpass. The road data may also comprise of data corresponding toquality of road, such as if a specified road is, e.g., rough or smooth.Building data may identify the structure involved, e.g., high-risebuilding, warehouse, or residential structure. Vehicle data may identifya means of travel, e.g., aerial, terrestrial, or marine, and whether thevehicle is autonomous, e.g., self-driving or human operated.Environmental data may identify the asset or vehicle's carbon footprint,e.g., carbon monoxide emissions.

The map may illustrate the various relationships between operation unitA 900, operation unit B 902, operation unit C 904, and operation unit D906 indicated by the arrows in the figure. In some embodiments, anadministrator may interact with the map via a graphical user interface(GUI) by selecting the displayed operation units to obtain more detailedinformation, such as relationships between the assets or elementsthemselves, vendor information and/or evaluative information.

Additionally, there are other factors that may come into play, forexample, capacity and flow rate at a specific facility might be tooslow, so part of the truck that is being automatically partiallyunloaded, may end up late to the second destination at the day. Thesystem and the method of the present invention may take intoconsideration these different factors to optimize the logisticoperation, such as by assigning the appropriate asset, e.g., driver orvehicle, to carry out the functions, and may dynamically and continuallyupdate the operation as it progresses. In addition to asset or elementcompatibility determinations based on their operational constraints,consideration may be given to financial constraints, environmentconstraints and/or geographic constraints to compute the optimizedoperational procedure.

A financial constraint can be any monetary or cost limitation to anaspect of the operations of a supply and distribution chain, such as,e.g., operating costs, asset maintenance costs, and legal expenses. Ingeneral, an operational constraint may be any element, factor, and/orsubsystem that limits or restricts an aspect or activity of theoperation based on functionality of the assets. Operational constraintsmay be fixed values, but can slightly alter with time due to assetdegradation, e.g., max distance may decrease over time due to engine andtransmission wear and tear. For example, warehouse loading docks maycomprise of structurally differing heights, and because the incline orelevation of the approach to the loading dock is variable, autonomousvehicles or assets may require this data ahead of time to determinewhether service to a particular location is possible. As anotherexample, a driver-related operational constraint may due to theirauthorization to operate certain types of vehicles that correspond totheir driver profile or driving behavior, e.g., large truck or smallcargo van. Operational constraints corresponding to road type mayinclude, e.g., weight and height limitations. Building type operationalconstraints may indicate limiting or restricting features of thebuilding, e.g., loading dock height, weight capacity, and cargocapacity. Operational constraints related to vehicle types may include,e.g., capacity, distance, maintenance schedule and driving schedulerequirements. Operational constraints related to environment type mayinvolve the use of green transportations, e.g., electric cars and hybridvehicles, to reduce greenhouse gases emission and to reduce fuelconsumption, and to be environmentally sustainable.

FIG. 10 illustrates a table of operational constraints corresponding toan operation unit, according to at least one embodiment. Operation unitD 1000 may correspond to vehicle types, and may comprise assets, e.g.,light or heavy capacity forklift, marine ship and manned or unmannedaerial vehicles (UAV), belonging to a company or fleet. The system andthe method of the present invention may allow an administrator tointeract with a GUI to pull up additional data about an operation unitand/or its assets or elements, such as to view table 1002. Table 1002may list all known operational constraints belonging to a particularasset or element, such as for a forklift in the example shown, e.g.,maximum weight, maximum dimension, maximum distance, and maximumvelocity. The operational constraints may be factored into theoptimization of a transit operation when determining assetcompatibilities to carry out the functions of the operation in order toavoid inefficiencies and inoperability. Aside from operationalconstraints, table 1002 may include, e.g., an identification of anassigned operator; a warehouse identification comprising a numeric oralphanumeric ID or code for specifying a particular warehouse in whichan asset is located; an asset name or code to indicate, for example,whether the resource is equipment controlled by a human operator ormachine; and a status indicator for indicating whether an asset isactive or inactive.

In addition to financial and operational constraints, the system and themethod of the present invention may consider environment constraints andgeographic constraints when optimizing a transit operation. Anenvironment constraint may be any limitation or restriction placed onthe logistic operation that is beyond the limitations or restrictions ofthe operation's assets, such as, e.g., a weather event, or delays ofcargo or inventory arriving and departing from a carrier outside of thenetwork. In contrast to operational constraints, environment constraintsmay be variable, dynamic, and difficult to accurately predict. Ageographic constraint may be any limitation or restriction due tolandscape or geography of the transit operation or route, such as, e.g.,mountains, bodies of water, and flat plains.

In some embodiments, the system and the method may assign a differentweight or priority to financial constraints, operational constraints,environment constraints and geographic constraints. For example, afinancial constraint such as a predetermined amount of funds dedicatedto a project or transit operation may take precedence over anoperational constraint whereby an asset that may not be the best choicefor a transit operation may be used, such as, e.g., shipping by multipletrucks over thousands of miles over an airplane. In this case, a singleairplane may have a larger capacity and be able to carry a larger loadthan the trucks, however, due to its higher cost-of-use the trucks maybe used instead. In one embodiment, financial constraints may have thehighest priority, followed by geographic constraints, environmentconstraints, and then operational constraints.

There are further benefits to the autonomous supply and distributionchain, such as, e.g., for vehicles operating 24 hours per day every dayof the year, except during maintenance procedures and fuel refills.Since vehicles do not have biological needs such as, e.g., sleep,scheduling requirements may be different from when human drivers areinvolved. Scheduling requirements may be a combination of demandrequirements and maintenance, and the maintenance schedule itself can beoptimized to avoid peak times, e.g., accelerated maintenance ahead ofChristmas, or preventative maintenance, e.g., particularly when highrates of pothole traversal are detected.

A plurality of assets, such as, e.g., trucks, trains, ships, airplanes,micro-drones, delivery drones, humanoid robots, self-driving orself-guided car robot drones, may be routed such that the vehicles arescheduled to arrive at a known bottleneck location at intentionallydelayed intervals to prevent delays at the location. For example, if toomany delivery machines arrive at a storage facility at the same time,there may be delays while the machines have to line up to load orunload. As each machine progresses on an assigned route, locationinformation may be used to continually update the route to minimizelatency at all junctures within the scope of the supply and distributionchain being optimized and monitored. Location information of thewhereabouts of the asset may also be used to update an estimated time ofarrival, such as, e.g., at a destination or at a bottleneck point, or tonotify downstream human or machine assets of an expectedarrival/departure time adjustment. In some embodiments, the congestionat a known bottleneck location may be mitigated by using multiple modesof transportations, such as, e.g., at a multi-modal facility. Forexample, if a bottleneck location is a multi-modal facility comprising arailway yard, a sea port, and an airport with limited capacities, allthree transportation modes (train, cargo ship and airplane) may be usedfor the transit operations in the queue instead of using only onetransportation mode for delivering goods, therefore alleviatingcongestion in loading and unloading activities in individual railwayyard, sea port, and airport. When a machine completes an assigned route,or a portion of a route that the system and method may detect as meetingcertain criteria, the machine may be ready to be re-routed, and theestimated durations of the remaining routes and the estimated times ofcompletion of machines currently on assigned routes are used to assignone of the pending, queued, or newly requested routes to the vehicle ormachine.

On the other hand, while having too many vehicles arriving at a specificlocation at a given time may cause inefficiencies, not having enoughvehicles arrive at that location for a period of time can also createinefficiencies by creating downtime in the system and in the supply anddistribution chain. For example, in order to prevent the downtime ofoperations that require a constant supply of materials or goods, thesystem and the method may autonomously optimize the operation to allowfor storage facilities on the premises such that a surplus of necessarygoods are stored and used in the operation as required. The storing ofsurplus may allow delivery vehicles to arrive at intermittent orconstant intervals such that there is always another vehicle ready toload or unload without large gaps of time in between, such as, e.g., inexcess of 30 minutes. In addition, for each portion of a transit, e.g.,a delivery job, an estimate of the time required for completion isdetermined. Using the estimated time, an initial route is determined foreach of the vehicles or machines in an operation or fleet, and iscontinually updated based on actual measurements to increase theaccuracy of the estimated completion times and to provide a moreaccurate scheduling of transits.

FIG. 11 illustrates an example process flowchart performed for providingan optimized transit operation, according to at least one embodiment.Operation 1110 requests for an optimization of transit routes of alogistics operation based on asset compatibilities. The supply anddistribution chain management server may be communicatively coupled to aGUI, allowing an administrator to inquire one or more assetrecommendations for implementation in a transit operation. For example,a recommendation can be sought for each aspect and/or phase of aninternational delivery of materials and goods to a client location, suchas, e.g., loading and unloading locations and times, driver type,vehicle type, road type, building type, and route. At operation 1120,relationships between operation units of the chain, and one or moreassets associated with each of the operation units are autonomouslymapped and analyzed via one or more relational databases using analgorithm. In addition, the databases may maintain information regardingassociated technology asset vendors, interoperability data, rankings,and any other information that may be relevant to determine an optimalor preferred transit operation protocol, thereby avoiding compatibilityissues. Operation 1130 determines and recommends a preferred transitoperation that may be based on the relationships between the operationunits and the corresponding assets or elements. Certain asset functionsand protocols may be limited by financial constraints, operationalconstraints, environment constraints and geographic constraints. Forexample, an aerial vessel may be used in lieu of a transit function thatwould typically utilize a marine ship, e.g., the marine ship'soperational constraint of speed may be higher, e.g., slower than that ofan aerial vessel, in order to fulfill a time limit due to anenvironmental constraint, e.g., sudden adverse weather conditions mayhave slowed down the rest of the operation. Operation 1140 repeats fromoperation 1110 at a predetermined time interval to continually updatethe transit operation.

In at least one embodiment, the present invention discloses a system anda method for providing inventory location identification, and dynamicmodification of transit operations or routes to alter one or moredestinations of the inventory while it is in transit to a new locationat any time. Dynamic modification may increase performance where itmatters in the supply and distribution chain by adding flexibility intothe system such that delays occurring from unexpected events areminimized. For example, an unforeseen weather event may prevent aparticular asset from completing its delivery job; however, the systemmay autonomously and dynamically modify the route to allow for anotherasset not impacted by the weather event to deliver a same or similaritem do the end user. Due to the rapid and fluid environment of modernwarehouses and transportation services, workers often receiveinstructions to change the destination of inventory that has alreadybeen received and is in the process of being transferred to anotherlocation. However, once the inventory has been picked-up from itsoriginal location, it may not be possible previously to determine thevehicle used for transport and its current location until the inventoryarrives at a warehouse and/or until an inventory database is updated. Ina busy delivery or supply and distribution chain environment, this canresult in substantial delays and inefficiencies due to the inability torapidly locate inventory and respond to urgent stock needs or change oftransfer orders.

Registrations of inventory location, e.g., in a bin or asset, may beimplemented in various ways. For example, bar codes and/or RFIDs may beused to label and identify the inventory, bins and/or assets. During apick-up or drop off of inventory, these bar codes and/or RFIDs may bescanned or read with a scanner operated by a warehouse worker and thecollected data communicated, e.g., using a wireless link or a wirednetwork, back to an inventory management system to register the locationof the inventory and trigger an update to the database. Alternatively,or additionally, a warehouse operator may radio or call-in the updatesto a central office where a human operator manually enters the updatesinto the inventory management system. Stock arriving and departing froma warehouse may also be scanned so that it is associated with an assetidentifier.

A typical warehouse includes storage areas for storing inventory. Thestorage areas may include rows of shelves that accommodate a largenumber of storage bins, which are usually labeled for ease ofidentification. An inventory may refer to an element or quantity ofstock in a facility, e.g., a warehouse, or on a vehicle, and can includeitems such as commercial products, e.g., books, office supplies,articles of clothing, electronic devices, home appliances, or othermerchandise. In some embodiments, inventory can also include a person orgroup of people to be transported. By way of example, inventory maycomprise any quantity or number of parts for manufacturing or providinga finished product, or any quantity or number of parts that are used forproviding a service.

During normal warehouse operations, there can be many requests fordifferent inventory items each day. In addition, inventory is oftenmoved from one location in the warehouse to another for a variety ofreasons. For example, it may be necessary to move inventory from one binlocation to another, to locate certain inventory in an area forinspection, and/or to prepare for shipment outside of the warehouse. Binidentifiers may comprise a data structure or record that providesinformation to identify the bins for storing and locating inventory.Typically, requests to move inventory are issued as transfer orders.When a warehouse worker is given a transfer order, the worker must firstlocate the desired inventory. A transfer order to transfer inventory toa new location usually includes the storage location information, whichis based on row and bin data retrieved from, for example, a computerizedinventory management system. Once the worker has located the inventory,the worker may need to use an asset to transport to its new location. Anasset may comprise any type of resource for moving or otherwise handlingstock in the warehouse, such as, e.g., light and heavy capacityforklifts, conveyors, trolleys, pushcarts, as well as human operatorsfor manually moving stock. Upon moving the stock from its currentlocation, the worker may use a scanner to scan a bar code or RFID taglocated on the inventory, and a bar code or RFID tag located on thecorresponding bin. An RFID tag may comprise an internal clock, amicroprocessor, memory, and at least one input interface for connectingwith sensors located in the asset or a telematics device. Theinformation is then transmitted and a database in the inventorymanagement system is updated to indicate that the particular inventoryis no longer located in the bin. Once the inventory arrives at its newlocation, the worker may use the scanner to update its location. Forexample, the worker may accomplish this by scanning the bar code or RFIDtag located on the inventory and scanning the bar code or RFID tagassociated with the inventory's new bin location. As a result, thedatabase is updated to indicate that the moved inventory is now locatedin its new location, whether it is in the same facility as the previouslocation or off-site, such as, e.g., to a yard or a port.

FIG. 12 is a schematic diagram of a distribution center comprising awarehouse, according to at least one embodiment. Warehouse 1200 includeone or more delivery and/or shipment area for receiving and shippinginventory, which may comprise shipping container 1202, and site vehicle1204 used to move shipping container 1202 around the distributioncenter. Sensor 1206 may be associated with container 1202 and/or vehicle1204 for gathering various data, such as, e.g., location and position.Warehouse 1200 may comprise shelf 1208 and bin 1210 for storinginventory. Both shelf 1208 and bin 1210 may be labeled for ease ofidentification and tracking. Labeling may be achieved through the use ofany type of indicia or marking, or electronically, such as, e.g., barcode or RFID tag. Warehouse 1200 may function as the main storagefacility of a supplier or merchant of goods or services, or it may beone of several storage facilities that are part of a supply chainnetwork, and may not be a separately or remotely located storagefacility, e.g., it may be co-located with a store-front or otherlocation for selling or otherwise providing goods or services to endusers.

Upon the removal of an inventory from a warehouse, the inventorymanagement system may be updated to indicate a vehicle location andposition that is being used to move the inventory and, thus, providevisibility of the inventory while it is in transit. After receiving atransfer order, the system and the method may initially associate aninventory identifier, e.g., RFID tag, belonging to a target inventorywith a first location identifier belonging to the first, e.g., original,location of the inventory. During the execution of the transfer order,the inventory identifier may be associated with an asset identifierbelonging to a transport vehicle or asset used to transport theinventory, which allows for visibility of the stock during itstransport. When the inventory arrives at a second location, whether itis the final destination or an intermediary stop on the route, theinventory identifier may then be associated with a second locationidentifier belonging to the second location. As a result, this allowsthe system and the method the ability to monitor each step of thetransit operation, and to alter the destination of the stock at anypoint during its transit. Such visibility and control may beadvantageous because during a typical day, there may be numerous changesin transfer orders or updates to inventory deliveries.

As with the inventory and bin identifiers, asset identifiers maycomprise various types or categories of data. Such data may uniquelyidentify an asset and may be organized into tables or any other suitabledata structure. As an example, an asset identifier may include dataindicating an asset ID or number, an asset name, an asset type, a username, e.g., name or employee number of human operator, and operatingdata indicating the basic characteristics of the asset, e.g., maximumspeed, and actual weight of the asset. Additionally, or alternatively,the asset identifier data may include status data, e.g., active orinactive. As another example, an inventory identifier may include dataindicating the name or owner of the inventory and the basiccharacteristics of the inventory, such as, e.g., weight, volume,dimensions, shelf life or expiration date. Additionally, oralternatively, the inventory identifier data may include a stock number,an inspection or warehouse log number, a received date and/or inventorystatus data, e.g., free or blocked.

The inventory management system can make updates when inventory isremoved from a bin or an asset. By way of example, assume there is atransfer order to move inventory from a first bin to a second bin. Whena warehouse worker removes the inventory from the first bin with anasset, such as with a forklift, the registration of the pick-up may bemade with the inventory management system. At this point, an update tothe database will be made to associate the inventory identifier for theinventory with the asset identifier for the asset transporting theinventory. When the inventory is relocated to the second bin andregistered with the inventory management system, another update may bemade so that the inventory identifier is associated with a binidentifier for the second bin. To associate an inventory identifier witha bin identifier or an asset identifier, various techniques may beemployed, such as, e.g., a table may be provided to associate each stockidentifier with a bin identifier and an asset identifier. In someembodiments, a relational database may be maintained, wherein inventoryidentifiers are stored and associated with bins and assets. Eachinventory identifier may be associated with a bin identifier or an assetidentifier depending on its location; thus, an examination of theinventory identifier may provide visibility into the location of theinventory, e.g., stored in a bin or with an asset.

FIG. 13 is a flowchart of a method for monitoring inventory location,according to at least one embodiment. Operation 1310 associates aninventory identifier of an inventory with a first location, such as,e.g., a bin within a storage facility. The information may be stored ina database of an inventory management system. Operation 1320 receives atransfer order to move the inventory to another location, such as, e.g.,within the same facility, a different facility within a network, or acustomer destination. For example, the inventory may be ordered by acustomer and requires to be delivered to a customer location. Operation1330 disassociates the inventory identifier from the first location andmay update the database to indicate that the inventory is no longer atthe first location. Operation 1340 associates the inventory identifierwith an asset for transporting the inventory. The asset may include,e.g., trucks, trains, ships, airplanes, micro-drones, delivery drones,humanoid robots, self-driving or self-guided car robot drones. Thedatabase may be updated to indicate that the inventory is now with theasset. Operation 1350 disassociates the inventory identifier from theasset when the asset has arrived at its intended destination. Operation1360 associates the inventory identifier with a second location. Thismethod may allow for visibility into the inventory's location at anypoint during the journey of the inventory from the first location to thesecond location. A change in transfer order, such as, e.g., a differentdestination or to return to the first location, may be readily fulfilledat any time.

Alternatively, or in addition to, the system and the method above forproviding visibility into an inventory's location at any point within acorresponding transit operation, a wireless tracking device or tag maybe used to monitor the inventory through a network, such as, e.g., theInternet of Things (IOT). IOT may be a network of physical devices,vehicles, appliances and other items embedded with electronics,software, sensors, actuators, and network connectivity, which may enablethese objects to connect and exchange data. Technologies such as GlobalPositioning System (GPS), Radio Frequency Identification (RFID), andGeneral Packet Radio Service (GPRS) may track and report movements ofthe inventory on which the tracking device is mounted. In addition tolocation, the device may collect, or be coupled to an external sensorfor collecting, sensory data such as for determining velocity, heading,vibration, acceleration, and data that may relate to the environment ofthe inventory. The autonomous electronic tracking devices may besituated in proximity to the inventory being shipped, such as, e.g.,affixed or coupled to a container of the inventory such that it issecurely sealing the container. The tracking device can be coupled tothe container before it begins its journey and may re-couple to thecontainer during the journey, such as, e.g., after authorized custominspections. In some embodiments, processes relating to the inspectionor acceptance of incoming inventory may be automated or autonomous, suchas, e.g., using X-ray scans of inventory containers. The tracking devicemay be programmed to wake up periodically, collect sensory data, and tocommunicate with a central server, such as, e.g., sending eventnotifications to the server. In general, each event notification mayinclude an identification of the event or event type, a location of theinventory when the event occurred, and additional details of the eventsuch as a date and/or time when the event occurred, the status of theinventory before, during, or after the event, or details on the movementof the inventory, e.g., a accelerometer reading from the tracking devicecoupled to the container.

The tracking device may report various events, including for example,security events, environmental events, process events, and geo-trackingevents. Security events may indicate whether the inventory or trackingdevice may have been tampered with. For example, the tracking device mayreport when a vertical or horizontal bolt securing the tracking deviceto a container is cut, indicating that the container was opened. Othertypes of tampers can also be detected, such as, e.g., shock intrusion orlight emission within the container that exceeds a threshold.Environmental events can indicate whether one or more environmentalvariables, such as, e.g., temperature, humidity, vibration, andacceleration in relation to an acceptable range, such as, e.g., arecommended range for the inventory. Process events may indicate thatvarious action or procedural events in the journey of the inventory haveoccurred. For example, process events may indicate that a trackingdevice has been attached to the container or detached from thecontainer, e.g., that the inventory is beginning or ending its journey.Geo-tracking events may be periodic reports of the location of thetracking device. For example, the tracking device may send a report ofits current location according to a schedule, such as, e.g., at fixedintervals of time. The central server may process the tracking events todetermine when an inventory has entered or left a predefined area. Forexample, the server may define geo-fences, e.g., a virtual perimeter,around important locations along the journey of the inventory, such as,e.g., ports, and the server may determine that the inventory has enteredor left a given location when the tracking device enters or leaves ageo-fence.

FIG. 14 is a schematic diagram of a tracking device coupled to acontainer of an inventory, according to at least one embodiment. Aprocessor 1410 such as, e.g., a microprocessor or microcontroller, maycontrol the operations of tracking device 1420 that is coupled withinventory 1430, such as, e.g., affixed to a container comprisinginventory 1430. Clock 1440 may trigger transceiver 1450 to transmit orreceive data on a periodic basis, such as, e.g., every 10 minutes.Processor 1410 may run on a plurality of clock 1440 such as a high speedclock may be designated for normal operation, and a slow speed clock maybe used when conserving power. Communicatively coupled to processor 1410are one or more sensors, such as, for example, temperature sensor 1460for monitoring temperature, and accelerometer 1470 for detectingvibrations or shocks to which inventory 1430 is subjected in transit.Output signals of the sensors may be transmitted to processor 1410,which may provide analog-to-digital conversion of the signals andformatting of the data for transmission by transceiver 1450. A signaltransmitted by transceiver 1450 may be received by a central server. Themonitored data for various classes of inventory 1430 may not be thesame. For some goods, temperature may be a critical environmentalfactor, and temperature sensor 1460 may therefore be provided. For goodsthat are sensitive to vibration or shock, accelerometer 1470 may beprovided. Because sensors placed in the cargo are desirably small andinexpensive, they may be battery powered, preferably with very lowaverage power consumption, and may remain in a sleep mode andperiodically power on to transmit or receive data at periodic intervalsby clock 1440.

FIG. 15 is a flowchart of a method for tracking an inventory using atracking device, according to at least one embodiment. Operation 1510affixes a tracking device to a container comprising an inventory to beshipped. The tracking device may comprise, or may be communicativelycoupled to, other sensors, such as, e.g., a temperature sensor or anaccelerometer. Operation 1520 collects location data or sensory data ofthe inventory's environment from the devices. Operation 1530 records thelocation data and the sensory data. Operation 1540 transmits thelocation data and the sensory data to a central server for processing.The device may be configured to transmit or receive data atpredetermined intervals, such as, e.g., every 15 minutes.

In at least one embodiment, the present invention discloses a system anda method for an autonomous supply and distribution chain managementnetwork. A supply and distribution chain may be any and all activitiesassociated with defining, designing, producing, receiving, delivering,monitoring, storing and using the components and sub-components used inmanufacturing a product. A server may control and coordinate theprocesses involved in distributing the product from suppliers to thecustomers, including generation of purchase orders and payment ofinvoices. Once a product is qualified, a defined set of interactions mayoccur in a particular sequence and at designated times that may permitthe supply and distribution chain to be synchronized between a customerand a supplier. Such a well synchronized chain may comprise minimalinventories and short reaction times to efficiently handle transferorder changes. The network may comprise modules pertaining to variousaspects and operations of a supply and distribution chaincommunicatively coupled to a centralized server, and may include, e.g.,sourcing, procurement, conversion, logistic, and collaboration. Theserver may manage individual modules independently or simultaneouslysuch that there is coordination in the supply and distribution chainnetwork. For example, the supply and distribution chain network mayconnect actors having various roles, such as, e.g., dock workers,longshoreman, field agents, customs agents, freight forwarders,customers, suppliers, logistics providers, carriers and financialinstitutions. The system and the method of the present invention maycreate a network which supports customers requesting a same or similarproduct, and may realize lower costs and increased flexibility even inchanging supply demands. Consider a customer X who orders supplies fromsuppliers A and B, but neither supplier A nor B have the inventories tomeet the needs of the customer. By managing the modules centrally andsimultaneously, the system may be able to determine that a supplier Chas extra supplies of the same type demanded by customer X, and thatanother customer Y orders from either supplier B or C for their needs.The supply chain server can then automatically determine usingoptimization algorithms to arrange for supplier C to ship supplies tocustomer X so that supplier B can ship supplies to customer Y. Inaddition, the server may consider user-defined constraints or employmachine learning processes when determining whether to proceed with theshipping operations, such as, e.g., the ability to determine whether toarrange for supplier B to ship supplies to customer Y and supplier C toship supplies to customer X is cost efficient.

Autonomous supply and distribution chain management may be the strategicmanagement and integration of operations involved in the acquisition andconversion of materials to a finished product delivered to an end user,such as, e.g., a customer, without human intervention. The server mayreceive a customer demand, e.g., a purchase order, for direct materialprocurement from a customer detailing the orders that the customer maydesire. In some embodiments, forecasts may be used to notify suppliersof future anticipated demands so that the suppliers can plan inventoryaccordingly. The supply and distribution chain server may check with oneor more suppliers to determine whether the demand can be fulfilled bythe suppliers. If the demand cannot be fulfilled by the suppliers, theserver may contact customers and suppliers and attempt to eitherredistribute the customers' demands to different suppliers or requestthat customers alter their demands. Additionally, the customer demandmay be validated to ensure that they conform with contract terms anddetails outlined during an initial customer set-up process, and thatthey do not contain errors, such as, e.g., syntax errors, missingpopulation of mandatory fields, and incorrect customer address. Contractterms may be between any party of the supply and distribution chain,such as, e.g., customers and suppliers. For example, certainjurisdictions may comprise union labor laws that protects human workers,such as, e.g., by setting a maximum amount of work hours in a day. Thisvalidation may also include verifying whether the customer demand iscomplete, ensuring that every part number exists in the supply anddistribution chain network, and/or that all required information iscomplete and accurate. If the customer demand is invalid, abnormal, orincomplete, the server may notify the customer or an external computer,network, or database system that something is wrong with their requestand that a resolution process may be initiated. In response to aninvalid customer demand, the server may send alerts to all potentiallyimpacted parties, including the employees of the supply and distributionchain network. In some embodiments, the supply and distribution servermay accumulate customer demands for the same or similar products priorto distribution from suppliers. By providing suppliers with a smallernumber of larger orders, a more efficient process may be realized.

The system and the method may also provide global asset visibility inreal-time, such as, e.g., for vehicles, facilities, personnel, realestates and other machines and structures. By having real-timevisibility into the location of each ship, container, airplane, and/ortruck in a single environment which may unify, aggregate, analyze,and/or optimize the supply and distribution chain, autonomous vehiclescan adjust their behavior based on near instant or dynamic changes. If aship or plane is arriving 16 hours late, the autonomous asset mayminimize dwell time or idle down time by accelerating other work in theoperation, while still being back in time to complete its task. Inanother instance of the autonomous supply and distribution chainnetwork, certain types of vehicles can be engaged in tasks that arehighly repetitive, such as, e.g., driving along a predefined path insidea city with the top sellers from an e-commerce retailer. In thissituation, if an e-commerce order is received, the vehicle in thevicinity will be so close to the delivery region, that it cantemporarily detour, make the autonomous delivery, and then resume thenext part of its predetermined route without adverse effect to theroute.

The system may either operate in a default autonomous mode wherebysupply and distribution chain processes are fully automated fromend-to-end without user intervention including having the ability toautonomously authorize complete cycles and automated processes of supplyand distribution chain operations, or it may operate in a user-guidedsemi-autonomous mode. The user-guided semi-autonomous mode may be usedto, e.g., override certain processes or procedures, check foravailability of assets, maintain visual inspection, and/or troubleshootassets and operations. The user may interact through a graphical userinterface, such as, e.g., a virtual or augmented reality environment, orthrough a voice-activated personal assistant. Similar to a human body'sautonomous nervous system unconsciously controlling vital organs andbiological functions while responding to stimuli, the autonomous systemand method of the present invention may respond and adapt to dynamicsupply and distribution chain demands in real-time. The system mayprovide automated functions that may accelerate or decelerate thelogistics supply pipeline by either speeding up or slowing down thevarious operations within downstream processes and their correspondingmodules, while eliminating or automating many of the labor-intensive andtime-consuming operations required in legacy systems. A graphical userinterface may be communicatively coupled to the server and may provideinteractive control to one or more users of the network. In someembodiments, a supply and distribution chain simulator may be used tohelp user management to make strategic decision. The simulator may mimicdifferent aspects in the chain, such as, e.g., demands, supplies,inventories, manufactures and transportation, before the operation isactually conducted. The user may view the predicted outcome of a givenoperation and may adjust parameters of the operation to further improvethe performances or to avoid complications.

FIG. 16 illustrates a supply and distribution chain management network,according to at least one embodiment. The network may comprise a centralserver 1600, which may control and coordinate processes involved indistributing a product from suppliers to customers and may include thegeneration of purchase orders and invoices. Central server 1600 may becommunicatively coupled to a sourcing module 1602, a procurement module1604, a conversion module 1606, a logistic module 1608, and acollaboration module 1610. Unlike prior art system that manages thefunctions of these modules independently without communication with oneanother, the modules of the present invention may autonomously operateconcurrently to provide supply and distribution chain functions, suchas, warehouse management, e.g., inventory analysis, cost optimization,space utilization analysis, barcode tracking for inventory and material,order management, information management, future expense prediction,demand shaping capabilities, and documentation accuracy verification;customer order management, e.g., revenue analysis, order informationaccuracy analysis, backorder reporting, order dispatch accuracymeasurement, and delivery error rate; and reverse logistics management,e.g., analysis of reasons for returns, analysis of return delivery time,and value of returns.

Sourcing module 1602 may identify, evaluate, and contract suppliers forparts and/or services upon receipt of a demand. Procurement module 1604may execute the purchasing process, such as, e.g., acquire supplies, rawmaterials, parts and/or services by executing the contracts with thepreferred suppliers and to autonomously achieve one or more goals asdefined by the system. Based on forecasted demand, suppliers canaccurately anticipate customer demand, and plan their procurement andproduction processes accordingly. As a result, suppliers may avoidunnecessary purchases of raw materials, eliminate manufacturingover-runs, prevent the need to store excess goods, allow for confidentlyreceiving a supplier's products ahead of a designated schedule, orreduce prices to move products off of warehouse shelves. Suppliers mayalso minimize excess capacity and capabilities by dynamically adjustingits resources based on actual or predicted requirements of marketdemands. Conversion module 1606 may build, repair or customize demandedgoods from the parts and/or services acquired by procurement module1604.

Logistic module 1608 may be involved in planning, implementing andcontrolling the transport and storage of goods and services from thepoint of origin to a logistics company, and from the company to a pointof consumption, such as, e.g., an end user. Logistic module 1608 mayprovide complete visibility into how finished goods are stored anddistributed, such as, e.g., adherence to global trade compliances,replenishment planning, order processing, transportation, security,fleet management, reverse-logistics, returns, and route planning. It mayoperate within one company or multiple companies to manage the transportof a product from the supplier to the end user, such as, e.g., toautomate load balancing of shipment cargos, coordinate a plurality ofwarehouses and transportation channels, and provide travel routeanalysis. Collaboration module 1610 may coordinate with channel partnersto design, customize, and implement solutions that match the customerdemand. For example, the system and the method of the present inventionmay allow business partners to collaborate through a central server ofthe supply and distribution chain, which may provide benefits ofleveraging resources, such as, e.g., operation units and assets, and toshare business knowledge and databases of suppliers and customers. Theserver may provide the various functions to each partner, such as, e.g.,adding or deleting partners, changing parameters of its own supply anddistribution chain such as, e.g., inventory and assets, and maydesignate authority over other partners in the chain through agreements,such as, e.g., to have one partner chosen as a dominant partner withbroader authority than the rest. Each partner may be able to configuretheir own supply and distribution chain profile to decide the data thatis viewable or modifiable by their chosen partners, such as, e.g.,through setting of a level of privilege on the data to be shared withthe partners, and to clearly define the extent of the collaboration. Byhaving a joint operation, all parties can receive current informationconcerning shipment of inventory to customers, and may provide thebenefits of being more cost-effective, efficient, and responsive todynamic market needs.

FIG. 17 is a flowchart of a method for processing customer demand fordirect material, according to at least one embodiment. Operation 1710receives a customer demand, e.g., a purchase order, for procurement ofdirect material to be used for manufacture of a product from a customerthrough a central server. The server may convert the demand into astandard format used to analyze the demand. Operation 1720 analyzes thecustomer demand to determine whether it is valid, such as, e.g., whetherit is complete, accurate, and complies with contract terms. Operation1730 determines whether every part number exists in the supply anddistribution chain and whether the customer demand adheres to agreed-tocapacities. This may allow the server to effectively meet all customerdemands within suppliers' capacity constraints. For example, if customerdemand is not greater than capacity, the server may proceed to the nextstep in the chain. Operation 1740 compares the customer demand toprevious buying patterns and future forecasts from the same customer ora different customer. Operation 1750 identifies one or more patterns inthe comparison, such as, e.g., whether the demand adheres to a previousbuying pattern or a future forecast. Operation 1760 communicates thedemand to a supplier if the demand is valid. Once customer demand isfulfilled, rules and activities used in performing financialtransactions such as billing and processing of customer payments may beused. If a customer decides to return a product procured through supplyand distribution chain network, a determination may be made whether thesupplier has replacement parts in inventory, and whether the customerneeds a replacement immediately, or if the replacement part demand canbe added to an existing queue or forecast. If the customer needs thereplacement part immediately, the supplier's available inventory may bea primary source; otherwise a different supply with a same or similarproduct from within the network is used.

Thus, by providing a supply and distribution chain server to handle manyof the processes previously performed by individual entities of theprior art, a more efficient and cost minimizing architecture may berealized. By consolidating the many functions of the chain, activitiesand tasks can be fully automated and streamlined across a company'sentire supplier network, and many of the steps and costs expended bycustomers and suppliers of prior art supply chains are eliminated. As aresult, retailers can build stronger relationships with vendors, betterassess and manage their performance, improve negotiations to leveragevolume or bulk discounts and other cost-cutting measures, appreciatelower expenses for freight, operate faster and more reliable deliveries,and have access to complete supply and distribution chain visibility.Suppliers may benefit from lower expenses, lower planning and productioncosts, lower inventory, improved deliveries, visibility of demand, loweroperating expenses, and reduced manufacturing costs from smootherproduction flows. This all leads to improved profitability while sellingat lower prices which, in turn, may increase demand and thus creating anupward spiral of events. Both customers and suppliers may have access toa secure web site hosted by a supply and distribution chain server thatmay provide valuable information that may not have been available in theprior art, such as, e.g., customer buying habits, and the size andgrowth rates of markets served.

Although demand and supply of products have been discussed, it should beclear that demand and supply of any resource, including services, isalso within the scope of the invention. The term “product” throughoutthe specification thus refers to any such resource or service. Forexample, customers could be individuals desiring bandwidth on aconnection line in a network. Suppliers would then be sources of networkbandwidth. Customers could also be, for example, individuals desiringairplane tickets or theater seats from corresponding suppliers. A numberof embodiments have been described. Nevertheless, it will be understoodthat various modifications may be made without departing from the spiritand scope of the claimed invention. In addition, the logic flowsdepicted in the figures do not require the particular order shown, orsequential order, to achieve desirable results. In addition, other stepsmay be provided, or steps may be eliminated, from the described flows,and other components may be added to, or removed from, the describedsystems. Accordingly, other embodiments are within the scope of thefollowing claims.

It may be appreciated that the various systems, methods, and apparatusdisclosed herein may be embodied in a machine-readable medium and/or amachine accessible medium, and/or may be performed in any order. Thestructures and modules in the figures may be shown as distinct andcommunicating with only a few specific structures and not others. Thestructures may be merged with each other, may perform overlappingfunctions, and may communicate with other structures not shown to beconnected in the figures. Accordingly, the specification and/or drawingsmay be regarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A machine-implemented method, comprising:coordinating at least one of a sourcing procedure, a procurementprocedure, a conversion procedure, a logistic procedure, and acollaboration procedure, wherein the coordinating is of an autonomousmode or a semi-autonomous mode, wherein the autonomous mode does notinclude operator intervention, wherein the semi-autonomous mode permitoperator intervention; receiving a customer demand; analyzing thecustomer demand to determine whether it is valid, wherein a validcustomer demand comprises compliance with a contract term; determininginventory within supplier capacity; receiving an asset's telematicsdata; calculating duration for one or more destinations; calculatingestimated arrival times of the destinations; and determining one or moreroutes to the destinations based on at least one of an assetcompatibility data, a financial constraint, an environment constraintand a geographic constraint.
 2. The method of claim 1, furthercomprising: wherein the asset compatibility data comprises operationalconstraints, and wherein the operational constraints comprise a driverconstraint, a vehicle constraint, a road constraint, a buildingconstraint, and an environmental constraint.
 3. The method of claim 1,further comprising: wherein the asset compatibility data comprisesmapping and analyzing one or more relational databases.
 4. The method ofclaim 1, further comprising: wherein determining the route comprisesassigning the one or more destinations to the asset based on theduration of the one or more destinations.
 5. The method of claim 1,further comprising: wherein the collaboration process comprises mixingclasses of vehicle assets.
 6. The method of claim 5, further comprising:wherein the mixing of vehicle asset classes comprises dynamicallyupdating its mixture as the route is in progress.
 7. The method of claim1, further comprising: re-routing the asset to a remaining assigned orunassigned destination after completion of each destination based ondata of the remaining destination.
 8. The method of claim 1, furthercomprising: continually receiving the asset's telematics data,calculating service duration for one or more destinations, calculatingestimated arrival times of the destinations, determining a deliveryroute to the destinations at predetermined intervals; and altering theone or more destinations of the route while the route is in progressbased on the telematics data, estimated arrival times, and deliveryroute.
 9. The method of claim 1, further comprising: incorporating abusiness intelligence data into the determining of the route, andwherein the business intelligence data includes an additional amount ofrisk and cost assessment data.
 10. The method of claim 1, furthercomprising: using artificial intelligence to determine underperformingassets based on the telematics data.
 11. A machine-implemented method,comprising: coordinating at least one of a sourcing procedure, aprocurement procedure, a conversion procedure, a logistic procedure, anda collaboration procedure, wherein the coordinating is of an autonomousmode or a semi-autonomous mode, wherein the autonomous mode does notinclude operator intervention, wherein the semi-autonomous mode permitoperator intervention; receiving a customer demand; analyzing thecustomer demand to determine whether it is valid; determining inventorywithin supplier capacity; and communicating the customer demand to asupplier if the demand is valid and the inventory is within suppliercapacity.
 12. The method of claim 11, further comprising: wherein thecollaboration process comprises permitting one or more network partnersof a supply and distribution chain to collaborate.
 13. The method ofclaim 11, further comprising: comparing the customer demand to at leastone of a previous buying pattern and a future forecast; and determiningwhether the customer demand matches with one or more buying patterns orfuture forecasts.
 14. A machine-implemented method, comprising:coordinating at least one of a sourcing procedure, a procurementprocedure, a conversion procedure, a logistic procedure, and acollaboration procedure, wherein the coordinating is of an autonomousmode or a semi-autonomous mode, wherein the autonomous mode does notinclude operator intervention, wherein the semi-autonomous mode permitoperator intervention; receiving a customer demand; and determining aroute to the destinations based on at least one of an assetcompatibility data, a financial constraint, an environment constraintand a geographic constraint.
 15. The machine-implemented method of claim14, further comprising: wherein the semi-autonomous mode provide asimulation result of a transit operation to the operator.
 16. Themachine-implemented method of claim 14, further comprising: attaching atag to the asset to be tracked; and collecting at least one of alocation data and a transit characteristic data from the tag.
 17. Themachine-implemented method of claim 16, further comprising: building apredictive model from the collected data, and wherein building thepredictive model comprises grouping the data based on a similaritycriterion.
 18. The machine-implemented method of claim 17, furthercomprising: wherein the grouping of the data is an exact match or iswithin a predetermined threshold of difference.
 19. Themachine-implemented method of claim 17, further comprising: comparing asubsequent data to the predictive model; and determining whether thesubsequent data falls within a parameter of the predictive model. 20.The machine-implemented method of claim 19, further comprising: addingthe subsequent data to the predictive model thereby generating anotherpredictive model.